Abstract. A large number of MPI implementations are currently available, each of which emphasize different aspects of high-performance computing or are intended to solve a specific research problem. The result is a myriad of incompatible MPI implementations, all of which require separate installation, and the combination of which present significant logistical challenges for end users. Building upon prior research, and influenced by experience gained from the code bases of the LAM/MPI, LA-MPI, and FT-MPI projects, Open MPI is an all-new, productionquality MPI-2 implementation that is fundamentally centered around component concepts. Open MPI provides a unique combination of novel features previously unavailable in an open-source, production-quality implementation of MPI. Its component architecture provides both a stable platform for third-party research as well as enabling the run-time composition of independent software add-ons. This paper presents a high-level overview the goals, design, and implementation of Open MPI. 1

Desktop resources are attractive for running computeintensive distributed applications. Several systems that aggregate these resources in desktop grids have been developed. While these systems have been successfully used for many high throughput applications there has been little insight into the detailed temporal structure of CPU availability of desktop grid resources. Yet, this structure is critical to characterize the utility of desktop grid platforms for both task parallel and even data parallel applications. We address the following questions: (i) What are the temporal characteristics of desktop CPU availability in an enterprise setting? (ii) How do these characteristics affect the utility of desktop grids? (iii) Based on these characteristics, can we construct a model of server "equivalents" for the desktop grids, which can be used to predict application performance ? We present measurements of an enterprise desktop grid with over 220 hosts running the Entropia commercial desktop grid software. We utilize these measurements to characterize CPU availability and develop a performance model for desktop grid applications for various task granularities, showing that there is an optimal task size. We then use a cluster equivalence metric to quantify the utility of the desktop grid relative to that of a dedicated cluster.

The Los Alamos Message Passing Interface (LA-MPI) is an end-to-end networkfailure-tolerant message-passing system designed for terascale clusters. LA-MPI is a standard-compliant implementation of MPI designed to tolerate networkrelated failures including I/O bus errors, network card errors, and wire-transmission

Large-scale parallel computing is relying increasingly on clusters with thousands of processors. At such large counts of compute nodes, faults are becoming common place. Current techniques to tolerate faults focus on reactive schemes to recover from faults and generally rely on a checkpoint/restart mechanism. Yet, in today’s systems, node failures can often be anticipated by detecting a deteriorating health status. Instead of a reactive scheme for fault tolerance (FT), we are promoting a proactive one where processes automatically migrate from “unhealthy ” nodes to healthy ones. Our approach relies on operating system virtualization techniques exemplified by but not limited to Xen. This paper contributes an automatic and transparent mechanism for proactive FT for arbitrary MPI applications. It leverages virtualization techniques combined with health monitoring

fault tolerant MPI

Abstract. A large number of MPI implementations are currently available, each of which emphasize different aspects of high-performance computing or are intended to solve a specific research problem. The result is a myriad of incompatible MPI implementations, all of which require separate installation, and the combination of which present significant logistical challenges for end users. Building upon prior research, and influenced by experience gained from the code bases of the LAM/MPI, LA-MPI, FT-MPI, and PACX-MPI projects, Open MPI is an all-new, productionquality MPI-2 implementation that is fundamentally centered around component concepts. Open MPI provides a unique combination of novel features previously unavailable in an open-source, production-quality implementation of MPI. Its component architecture provides both a stable platform for third-party research as well as enabling the run-time composition of independent software add-ons. This paper presents a high-level overview the goals, design, and implementation of Open MPI, as well as performance results for it’s point-to-point implementation. 1

Parallel computing is now popular and mainstream, but performance and ease-of-use remain elusive to many endusers. There exists a need for performance improvements that can be easily retrofitted to existing parallel applications. In this paper we present MPI process swapping, a simple performance enhancing add-on to the MPI programming paradigm. MPI process swapping improves performance by dynamically choosing the best available resources throughout application execution, using MPI process over-allocation and real-time performance measurement. Swapping provides fully automated performance monitoring and process management, and a rich set of primitives to control execution behavior manually or through an external tool. Swapping, as defined in this implementation, can be added to iterative MPI applications and requires as few as three lines of source code change. We verify our design for a particle dynamics application on desktop resources within a production commercial environment. 1.

Ultra-scale computer clusters with high speed interconnects, such as InfiniBand, are being widely deployed for their excellent performance and cost effectiveness. However, the failure rate on these clusters also increases along with their augmented number of components. Thus, it becomes critical for such systems to be equipped with fault tolerance support. In this paper, we present our design and implementation of checkpoint/restart framework for MPI programs running over InfiniBand clusters. Our design enables low-overhead, application-transparent checkpointing. It uses coordinated protocol to save the current state of the whole MPI job to reliable storage, which allows users to perform rollback recovery if the system runs into faulty states later. Our solution has been incorporated into MVAPICH2, an open-source high performance MPI-2 implementation over InfiniBand. Performance evaluation of this implementation has been carried out using NAS benchmarks, HPL benchmark, and a real-world application called GROMACS. Experimental results indicate that in our design, the overhead to take checkpoints is low, and the performance impact for checkpointing applications periodically is insignificant. For example, time for checkpointing GROMACS is less than 0.3 % of the execution time, and its performance only decreases by 4 % with checkpoints taken every minute. To the best of our knowledge, this work is the first report of checkpoint/restart support for MPI over InfiniBand clusters in the literature. 1

We discuss the unique architectural elements of the Los Alamos Message Passing Interface (LA-MPI), a high-performance, network-fault-tolerant, thread-safe MPI library. LA-MPI is designed for use on terascale clusters which are inherently unreliable due to their sheer number of system components and tradeoffs between cost and performance. We examine in detail the design concepts used to implement LA-MPI. These include reliability features, such as applicationlevel checksumming, message retransmission, and automatic message re-routing. Other key performance enhancing features, such as concurrent message routing over multiple, diverse network adapters and protocols, and communication-specific optimizations (e.g., shared memory) are examined. 1

Piccolo is a new data-centric programming model for writing parallel in-memory applications in data centers. Unlike existing data-flow models, Piccolo allows computation running on different machines to share distributed, mutable state via a key-value table interface. Piccolo enables efficient application implementations. In particular, applications can specify locality policies to exploit the locality of shared state access and Piccolo’s run-time automatically resolves write-write conflicts using userdefined accumulation functions. Using Piccolo, we have implemented applications for several problem domains, including the PageRank algorithm, k-means clustering and a distributed crawler. Experiments using 100 Amazon EC2 instances and a 12 machine cluster show Piccolo to be faster than existing data flow models for many problems, while providing similar fault-tolerance guarantees and a convenient programming interface. 1